moai-cc-memory

Context loss in extended sessions occurs when token budgets reset between interactions. Memory management strategies persist important context across session boundaries using memory budgeting, context segmentation, and file-based storage to retain knowledge and working state automatically.

Session memory management applies memory seeding to load prior context, context window budgeting to allocate token capacity efficiently, and cleanup automation to remove stale data. This approach maintains continuity across multi-step workflows and persistent storage systems.

Context persistence stores working memory in file-based systems and retrieves it when token capacity resets. The Skill segments long-term memory separately from working memory, enabling knowledge retention and retrieval even when individual session windows exhaust their token allocations.

Memory cleanup automation removes redundant or expired context entries based on rules you define. This process integrates with context window budgeting to reclaim token capacity and ensures only relevant knowledge persists, optimizing context allocation across sessions.

Context persistence depends on file system availability and retrieval latency. Knowledge retention scales with persistent storage capacity, and memory fragmentation can occur if cleanup isn't tuned properly, requiring periodic rebalancing of stored context segments.